Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study

Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study

Abstract Background Predictive models for mental disorders or behaviors (e.g., suicide) have been successfully developed at the level of populations, yet current demographic and clinical variables are neither sensitive nor specific enough for making individual clinical predictions. Forecasting episo...

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Bibliographic Details
Main Authors: Abigail Ortiz, Arend Hintze, Rachael Burnett, Christina Gonzalez-Torres, Samantha Unger, Dandan Yang, Jingshan Miao, Martin Alda, Benoit H. Mulsant
Format: Article
Language:English
Published: BMC 2022-04-01
Series:BMC Psychiatry
Subjects:
Bipolar disorder
Episode prediction
Wearable device
Machine learning
Online Access:https://doi.org/10.1186/s12888-022-03923-1

Internet

https://doi.org/10.1186/s12888-022-03923-1

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